1. Field of the Invention
The present invention relates to power amplifier circuits and radio frequency transmitter systems, and particularly to a behavioral model and predistorter for modeling and reducing nonlinear effects in power amplifiers, the behavioral model and predistorter function having a sequential method for efficiently estimating its size.
2. Description of the Related Art
Recent advances in modern communication and broadcasting applications have led to the development of spectral-efficient modulation and access techniques that unavoidably result in time domain signals having strong envelope fluctuations. This amplitude modulation of communication and broadcasting signals allows for the transmission of higher data rates at the expense of stringent linearity requirements at the radiofrequency (RF) front end. The need for linearity is mainly motivated by two major concerns, including avoiding spectrum regrowth to minimize inter-channel interference, and reducing in-band error to maintain the signal quality while limiting the error vector magnitude at the transmitter and the bit error rate at the receiver.
Signal distortions at the RF front end result primarily from the nonlinear distortions of power amplifiers. These distortions are commonly compensated for at the transmitter using the baseband digital predistortion technique. Digital predistortion consists of implementing, before the power amplifier (PA), a nonlinear function that is complementary to that of the PA so that the cascade composed of the digital predistorter (DPD) and the PA operates as a linear amplification system. The wide adoption of digital predistortion increased the importance of modeling the nonlinear behavior of power amplifiers. Indeed, digital predistortion is a behavioral modeling problem in which the input and output of the system are swapped.
Moreover, behavioral modeling is essential for system-level simulations and the assessment of the expected linearity performance of an amplifier and a transmitter. Numerous behavioral modeling and digital predistortion structures have been proposed to accurately model and compensate for the dynamic nonlinear behavior of power amplifiers using memory polynomial (MP), two-box based structures, Volterra series, and neural networks.
In modern applications, high-efficiency power amplification structures, such as Doherty power amplifiers and envelope tracking power amplifiers, are of prime interest. These amplifier circuits often result in strongly nonlinear dynamic behavior, especially when they are driven with wideband modulated signals. This calls for the use of multi-basis functions memory polynomial structures involving dynamic nonlinear cross-terms, such as the generalized memory polynomial (GMP) model. Unfortunately, the performance gain achieved by the generalized memory polynomial model comes at the expense of a high complexity due to its large number of coefficients. To circumvent this limitation, the GMP model was applied within a twin-nonlinear two-box structure to reduce the total size of the model coefficients while maintaining its accuracy. However, one challenge is still associated with the GMP-based two-box model. This challenge is related to the evaluation of the model size, which involves determining eight parameters, including nonlinearity orders, memory depths, and leading and lagging cross-terms orders. In fact, it is essential to accurately estimate the size of the model in order to avoid suboptimal performance due to model under-sizing, and unnecessary computational load and numerical stability issues due to model over-sizing.
Thus, a behavioral model and predistorter for modeling and reducing nonlinear effects in power amplifiers solving the aforementioned problems is desired.